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针对实际工程中噪声难以避免和预测的问题,提出了鲁棒性较强的加权模糊树(W-FT)算法,采用基于局部异常因子(LOF)的加权最小二乘法代替最小二乘法学习模糊规则的后件参数,通过2个典型的非线性例子验证了该算法的有效性.应用W-FT算法建立了电站锅炉NOx排放特性模型,并与其他建模方法所建模型进行了对比.结果表明:所提出的W-FT算法能够有效地辨识噪声和异常值,具有较强的鲁棒性,所建立的模型预测精度较高,泛化能力较强.
Aiming at the problem that the noise is hard to be avoided and predicted in practice, a robust robust weighted fuzzy tree (W-FT) algorithm is proposed, which uses weighted least squares based on local anomaly (LOF) instead of least squares to learn fuzzy rules The validity of this algorithm is verified by two typical nonlinear examples.The model of NOx emission characteristics of utility boiler is established by using W-FT algorithm and compared with other modeling methods.The results show that : The proposed W-FT algorithm can effectively identify noise and outliers, has strong robustness, and the established model has higher prediction accuracy and better generalization ability.